Agentic Analytics Reviews and Ratings

Mandatory Features:

• Agent workflow orchestration: The ability for platforms to semi-autonomously or autonomously manage tasks to achieve specified goals. These platforms feature adaptive workflow design, self-configuring process flows based on new data inputs or evolving objectives, real-time decision making regarding task sequencing, and automatic error detection and recovery mechanisms. AI agents can dynamically allocate and optimize resources, continuously learning and improving orchestration strategies.

• Natural language query: The ability to enable users to interact with data using natural language, either typed or spoken. Advanced chatbots facilitate conversational Q&A, delivering data-to-insight interactions with full data visualizations. The platforms can group, filter and rank answers, providing comparisons, trend analysis, and geospatial and temporal understanding. They utilize temporal algorithms (e.g., time since/until, longer/slower) and geospatial algorithms (e.g., distance, route), presenting answers as maps. AI-enabled predictions and forecasting come with explanations and customizations, enhancing user engagement and understanding.

• Data preparation: The ability to combine data from various sources to create analytics models, including user-defined measures, data pipelines, sets, groups and hierarchies. AI agents can autonomously explore and understand complex data structures, dynamically addressing quality issues and inconsistencies. Additionally, they can comprehend relationships between data attributes, automatically suggesting meaningful joins and integrations while recognizing semantic connections across disparate sources. They infer potential data transformations based on historical patterns and business context, sharing insights and best practices across different data preparation tasks.

• Automated insights: The ability to employ machine learning techniques to automatically generate insights for end users, such as identifying key attributes in a dataset. AI agents proactively identify significant patterns, trends and anomalies, generating human-readable narratives that explain data findings. They provide probabilistic confidence scores for each insight and contextualize them within broader business performance frameworks. Responsible insight generation includes audit trails, explicit confidence scoring, uncertainty quantification, statistical significance and potential limitations. The platforms also detect algorithmic bias across multiple dimensions and offer human oversight options.

• Data source connectivity: The ability to handle multimodal data input and produce multistructured analytics outputs. Agentic analytics seamlessly integrate a wide range of data types — structured, semistructured and unstructured — from both internal and external sources. This includes diverse formats such as text, audio, images and video, enabling comprehensive analytics.